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Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.

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The AI Journey: What Is Real, and What Is AI?

by Lynne Ellyn

Cutter Consortium Fellow Lynne Ellyn recounts her experiences with AI technology in the real world, surveys the current landscape, and identifies key nontechnical issues that companies are likely to face when deploying AI-based systems.


Strategic Perspectives on AI Product Development

by Pavankumar Mulgund, by Sam Marrazzo

As AI becomes more visible as a corporate strategic tool, organizations will have to incorporate issues surrounding AI as part of corporate strategy. Pavankumar Mulgund and Sam Marrazzo help us by providing a framework for developing an AI strategy. The authors discuss the “minimum viable model” approach to the development of the underlying AI/ML models, along with the platform on which these models run and the inevitable tradeoffs. They conclude their piece by examining some best practices for the successful implementation of AI initiatives.


When AI Nudging Goes Wrong

by Richard Veryard

One way of getting an off-course system (or person) back on track is by nudging. This concept can be particularly useful in goal-directed systems. But, to reiterate, errors will occur. In his article, Richard Veryard describes technologically mediated nudging; the possible unintended consequences; and the need to consider the planning, design and testing, and operation of the system for robust and responsible nudging.


Vulnerability and Risk Mitigation in AI and Machine Learning

by David Biros, by Madhav Sharma, by Jacob Biros

Experienced IT practitioners know that errors will occur. A big part of building and managing complex systems is dealing with risk management (which includes identification and mitigation strategies). This is hard enough when documentation and source code exist. But the current state of ML-based AI tends to result in opaque black boxes, which make this activity, um, challenging. David Biros, Madhav Sharma, and Jacob Biros explore the implications for organizations and their processes.


Machine Learning and Business Processes: Transparency First

by William Jolitz

This article takes us to outer space (well, low Earth orbit, actually) to examine the issues around AI (in its ML incarnation) employed in a NASA system to track orbital debris. William Jolitz, the inventor of OpenBSD (open source Berkeley Software Distribution), makes the case for organization-wide awareness and alignment around ML and suggests that, like security, transparency cannot be bolted on later; it must be addressed at a project’s origin.

caution AI

Caution! AI Consequences Ahead — Opening Statement

by Lou Mazzucchelli

The contributions in this issue of CBTJ will help us get up to speed with the current state of AI and to think about some of the issues raised when we look beyond systems that appear to work as intended. Our contributors span industry and academia, and their commentary provides a good way to gain an overview of the problem.

GDPR and Beyond

GDPR and Beyond: Data Protection and Privacy Practices

by Curt Hall

It’s clear that organizations are going to have to extend their enterprise data protection practices to be more transparent and flexible if they hope to comply with the various (changing) requirements of data protection and privacy legislation.


Take a Data-Led Approach to Risk

by Tom Teixeira, by Jamie Gale, by Immanuel Kemp

Despite the emphasis on and investment in cyber security, traditional approaches are failing to protect businesses and their customers. In this Advisor, we explore the benefits of adopting a new, unified approach that brings together technology and risk management processes, enabling organizations to better protect themselves against cyber threats and safeguarding their businesses, data, and revenues.